Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM-CART Model

作者：Seera Manjeevan^*; Lim Chee Peng; Ishak Dahaman; Singh Harapajan

来源：IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(1): 97-108.

DOI：10.1109/TNNLS.2011.2178443

• Classification and regression tree
• fault detection and diagnosis
• fuzzy min-max neural network
• induction motor
• motor current signature analysis

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

• 出版日期2012-1